An Adaptive Task Scheduling Based Energy Optimization Model for IoT Devices

Abstract

This research presents a novel energy optimization model for Internet of Things (IoT) devices by integrating adaptive task scheduling algorithms with dynamic energy management mechanisms to enhance energy efficiency in IoT systems. We develop an innovative Dual-Factor Adaptive Efficiency Function (DFAEF) that comprehensively considers both device workload and battery status to achieve precise efficiency evaluation. The model employs a multi-parameter weighted scoring mechanism for task allocation and enhances overall system performance through network topology optimization. Simulation results demonstrate that, compared to traditional methods, the proposed model reduces energy consumption by approximately 31% while maintaining high task distribution balance and network connectivity. This research provides IoT system designers with a practical energy optimization solution that offers significant value for extending battery-powered device lifespan and enabling sustainable operations.